scholarly journals Optimization guided lower and upper bounds for the resource investment problem

2001 ◽  
Vol 52 (3) ◽  
pp. 340-351 ◽  
Author(s):  
A Drexl ◽  
A Kimms
2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Behrouz Afshar-Nadjafi ◽  
Mohammad Arani

The preemptive Multimode resource investment problem is investigated. The Objective is to minimize the total renewable/nonrenewable resource costs and earliness-tardiness costs by a given project deadline and due dates for activities. In this problem setting preemption is allowed with no setup cost or time. The project contains activities interrelated by finish-start type precedence relations with a time lag of zero, which require a set of renewable and nonrenewable resources. The problem formed in this way is an NP-hard. A mixed integer programming formulation is proposed for the problem and parameters tuned genetic algorithm (GA) is proposed to solve it. To evaluate the performance of the proposed algorithm, 120 test problems are used. Comparative statistical results reveal that the proposed GA is efficient and effective in terms of the objective function and computational times.


2019 ◽  
Vol 39 (4) ◽  
pp. 532-547 ◽  
Author(s):  
Yifei Ren ◽  
Zhiqiang Lu

Purpose In response to the station design and flexible resources allocation of the aircraft moving assembly line, a new problem named flexible resource investment problem based on project splitting (FRIP_PS), which minimizes total cost of resources with a given deadline are proposed in this paper. Design/methodology/approach First, a corresponding mathematical model considering project splitting is constructed, which needs to be simultaneously determined together with job scheduling to acquire the optimized project scheduling scheme and resource configurations. Then, an integrated nested optimization algorithm including project splitting policy and job scheduling policy is designed in this paper. In the first stage of the algorithm, a heuristic algorithm designed to get the project splitting scheme and then in the second stage a genetic algorithm with local prospective scheduling strategy is adopted to solve the flexible resource investment problem. Findings The heuristic algorithm of project splitting gets better project splitting results through the job shift selection strategy and meanwhile guides the algorithm of the second stage. Furthermore, the genetic algorithm solves resources allocation and job schedule through evaluation rules which can effectively solve the delayed execution of jobs because of improper allocation of flexible resources. Originality/value This paper represents a new extension of the resource investment problem based on aircraft moving assembly line. An effective integrated nested optimization algorithm is proposed to specify station splitting scheme, job scheduling scheme and resources allocation in the assembly lines, which is significant for practical engineering applications.


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